Fast subgroup discovery for continuous target concepts

Abstract

Subgroup discovery is a flexible data mining method for a broad range of applications. It considers a given property of interest (target concept), and aims to discover interesting subgroups with respect to this concept. In this paper, we especially focus on the handling of continuous target variables and describe an approach for fast and efficient subgroup discovery for such target concepts. We propose novel formalizations of effective pruning strategies for reducing the search space, and we present the SD-Map*algorithm that enables fast subgroup discovery for continuous target concepts. The approach is evaluated using real-world data from the industrial domain. © 2009 Springer Berlin Heidelberg.